The wrong skewness problem in stochastic frontier analysis: a review

Alecos Papadopoulos and Christopher F. Parmeter ()
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Christopher F. Parmeter: University of Miami

Journal of Productivity Analysis, 2024, vol. 61, issue 2, No 3, 134 pages

Abstract: Abstract We provide a review of the literature related to the “wrong skewness problem” in stochastic frontier analysis. We identify two distinct approaches, one treating the phenomenon as a signal from the data that the underlying structure has some special characteristics that allow inefficiency to co-exist with “wrong” skewness, the other treating it as a sample-failure problem. Each leads to different treatments, while siding with either raises certain methodological issues, and we explore them. We offer simulation evidence that the wrong skewness as a sample problem likely comes from how the noise component of the composite error term has been realized in the sample, which points towards a new way to handle the problem. We also investigate the issues that arise when attempting to use the unconstrained Normal-Half Normal (Skew Normal) likelihood.

Keywords: Stochastic frontier; Wrong skewness; Non-representative sample; Skew Normal; Data mining; Singular Information matrix (search for similar items in EconPapers)
JEL-codes: C18 C21 C46 C51 D24 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11123-023-00708-w

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